algochains-library-mcp 0.1.2

MCP server for Claude Desktop: natural-language market research and strategy backtesting with the AlgoChains library.

AlgoChains Library MCP

Ask market-research and trading-strategy questions in plain English, and let Claude pull data with AlgoChains, backtest strategies, and return interactive charts inline — built for Claude Desktop via the Model Context Protocol.

No coding required: describe the strategy, get a full backtest dashboard back in the chat.

Install (easiest — one click)

1. Get your API key at https://algochains.ai.
2. Download the extension: algochains-library-mcp.mcpb.
3. Double-click the downloaded file (or in Claude Desktop: Settings → Extensions → Install extension… and pick it). Claude Desktop shows an install dialog — paste your API key into the AlgoChains API Key field and confirm.
4. Fully quit Claude Desktop (Cmd+Q on macOS / quit from the system tray on Windows — closing the window is not enough) and reopen it.

The first launch downloads the Python environment (a few hundred MB) — it can take several minutes and Claude Desktop may report the server disconnected once; it recovers on the next launch. The AlgoChains tools then appear under the tools icon in the chat box.

Example asks

Research:

"For crypto, pick a highly-traded coin and show trading hours in 30-min bins over the last 3 months. Highlight the busiest hours and shade market opens for London (blue), China (red), Brazil (green), with NYSE as the base case."

Build & backtest:

"Build an opening-range-breakout on SPY — range = first 10 min of the NY session, relaxed 2.5% trailing stop loss, force-exit at 12:00 ET, over the past 2 months."

What it does

Kind	Name	What it does
resource	algochains://agents-guide	The library's AGENTS.md — API, symbols, resolutions, examples (plus the server's determinism rules).
tool	research_query	Historical OHLCV for one symbol or a list — a quick look (wraps algochains.research_query).
tool	run_python	Run Python in a temp dir; any matplotlib figure is returned inline (themed).
tool	build_report	Run analysis cells in a temp dir; charts inline + report as markdown text.
tool	build_algorithm	Write a bt.Strategy and backtest it in the background; keeps the CSVs in a per-name temp dir (no plotting).
tool	make_backtest_dashboard	Render the run (waits if it's still running): fast matplotlib PNG inline + a React+Recharts interactive artifact.
tool	send_signal	Propagate a live trade signal (requires SIGNAL_SECRET; off by default).

Claude reads the resource to ground itself in the library, then uses the tools to answer.

Backtesting is two steps, mirroring research → build_report:

1. build_algorithm(name, ...) launches the backtest in the background (Backtrader is slow — long or multi-symbol runs take minutes) and returns quickly with state:"done" or "running".
2. make_backtest_dashboard(name) waits for the run if needed, then renders a matplotlib PNG inline plus artifact_jsx — a React+Recharts component Claude drops into an artifact for an interactive dashboard (candlestick equity, drawdown, rolling Sharpe, per-trade P&L, round trips).

What's written to disk (and why)

Almost nothing, and nothing hidden. Research code and backtests run in throwaway temp folders that clean themselves up — a backtest's raw CSVs stick around for ~12 hours in case you want to inspect them, then they're deleted automatically.

The one thing that's kept: every completed backtest leaves you a receipt. Three small files land in ~/Downloads/AlgoChains/comparisons/<run-name>/ (or ~/Desktop/… if you have no Downloads folder):

• strategy.py — the exact code that ran. Not a description of your strategy, the literal script that produced the numbers you're looking at.
• round_trips.csv — every trade it made: entries, exits, P&L.
• dashboard.png — the dashboard image itself, so the picture of the results survives along with the numbers. (not including the trades at the bottom for now, soon to come)

Here's why that matters. A chat scrolls away, but a trading result you might act on shouldn't. When Claude tells you a strategy returned +12%, these files are your proof of what returned +12% — so next week you can reread the logic, diff two variants of a strategy to see exactly what changed, or hand the script to someone else to reproduce your result. Without them, "the backtest I ran last Tuesday" would be a memory; with them it's a folder. And because the dashboard is saved too, you can simply ask Claude "show me the dashboard for <run-name> again" in any later chat — even after the run's working data has long been cleaned up.

Nothing else is saved, the files are a few KB per run, re-running a name simply overwrites its folder, and deleting any of it is always safe. (Power users can move the location with ALGOCHAINS_LIBRARY_MCP_COMPARISONS_DIR.)

run_python, build_report, and build_algorithm execute model-written Python locally on your machine — that's how Desktop (no code execution of its own) does the analysis. Only connect this server in an environment you trust.

Notes

• Symbol formats: Forex EUR-USD, Stocks AAPL, Crypto BTC-USD.
• Multiple symbols: research_query and build_algorithm accept a list of symbols as well as a single string. Multi-symbol backtests use the ('symbols', []) param and self.datas[i] (see the agents-guide "Multi-asset" section); the equity curve is the true combined portfolio value.
• Resolutions: 1m, 5m, 15m, 30m, 1h, 4h, 1d. Databases: Forex, Stocks, Crypto.
• Charts use a shared matplotlib/mplfinance theme; the interactive dashboard is React+Recharts (Plotly and other CDN-loaded libraries fail in Desktop's artifact sandbox — Recharts is bundled).